In this video it is shown how user-defined problems can be used to extend HeuristicLab with new custom optimization problems. As an example a user-defined n-queens problem is created and you can see how to define the problem's parameters and its solution encoding and how to implement a custom evaluation function using a programmable operator.

Hive is HeuristicLab's distributed computing infrastructure and can be used to execute experiments in a massively parallel and distributed fashion. This video shows how to create an experiment, upload it to Hive for execution and view the results.

In this tutorial HeuristicLab's scripting functionalities are demonstrated by simple examples. All important aspects of the scripting environment are explained by starting with the implementation of the simple "hello world" program and ending with a quick insight into the source code of an Offspring Selection Genetic Algorithm that solves a Quadratic Assignment Problem using HeuristicLab's rich API. Further use cases are available as script samples in HeuristicLab.

Application-specific Tutorials

This tutorial covers the basic functionality for symbolic regression and for analyzing symbolic regression models in HeuristicLab.

First, we demonstrate how to load data and how to use genetic programming to produce symbolic regression models. After that, all charts and visualizations for symbolic regression models are shown and the functionality for model analysis, simplification, and tuning is explained in detail. At the end of this tutorial we show how symbolic regression models can be exported to MATLAB, LaTeX and Excel.

In this video tutorial the basic steps necessary to perform symbolic classification with HeuristicLab are covered. As exemplary data the mammography dataset from the UCI machine learning repository is chosen and modeled by genetic programming. You can see how the problem and the algorithm are configured and after the algorithm is finished the resulting model is analyzed.